We compare our method with previous SOTA methods. 1) FUNIT[1]} is a early work on Few-shot image translation in an Unsupervised way which introduces two different encoders and an AdaIN module to generate a new image with mixed content and style. 2) DG-Font[2]} is an Unsupervised network using Deformable Convolution in Generator to achieve a better effect on cursive characters 3) MX-Font[3]} adopts multiple experts to extract different local structures which makes cross-lingual generation task possible. (4) AGIS-net[4]} uses two different decoders to generate images with shape and texture information which makes generated image more stable. (5) LF-Font [5]} proposes localized style representation which makes it enable to extract the component-wise features. For a fair comparison, we choose the Kaiti Line Font \(\footnote {Font is available at https://chanind.github.io/hanzi-writer-data/}\)  as the standard font and re-train all models on the training datasets in Sec. REF .
